贮藏期内灵武长枣果糖含量的高光谱预测

doi:10.3964/j.issn.1000-0593(2019)10-3261-06

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Abstract Hyperspectral imaging technique which is a non-destructive method combines image and spectral techniques to obtain image and spectral information of target objects’ and qualitative and quantitative analysis using spectral data has been widely used in the field of agricultural product testing. This paper uses visible/near-infrared spectroscopic imaging technique combined with chemometrics methods to achieve the non-destructive detection of fructose content of Lingwu long jujube during storage. The chemical value of jujube fructose was determined by High performance liquid chromatography (HPLC), and the hyperspectral images of long jujubes were collected using near-infrared hyperspectral system, and the average spectral data for each sample area of interest were extracted. Support Vector Machine With RBF Nucleus (RBF-SVM) Model for establishing storage time of long jujube. Orthogonal Signal Correction (OSC), Multiple Scatter Correction (MSC), Median Filter (MF), Savitzky-Golay (SG), Normalize (Nor), Gaussian filter (GF) and Standard Normalized Variate (SNV) were used to preprocess the original spectral data. To reduce the amount and dimension of data, the characteristic wavelengths were extracted by Backward interval Partial Least Squares (BiPLS), Interval Random Frog(IRF) and Competitive Adaptive Reweighted Sampling (CARS); the partial least squares regression( PLSR) model and principle component regression (PCR) were established based on full spectra and characteristic wavelengths for predicting fructose of Lingwu long jujube. The results indicated that the accuracy of the RBF-SVM model calibration set was 98.04%, and the accuracy of the prediction set was 97.14%, which could well predict the storage time of the jujube; The BiPLS, IRF and CARS methods were used to select characteristic wavelengths with 100, 63 and 23 from 125 wavelengths, respectively. In order to simplify the model and improve the accuracy of prediction of the model, the CARS algorithm was used to perform secondary extracted characteristic wavelengths of BiPLS and IRF and select characteristic wavelengths with 18 and 15, respectively, which significantly reduced the number of characteristic wavelengths. Comparing models of the full band spectrum with the models of extracted characteristic wavelengths of PLSR and PCR, PLSR model based on the characteristic variables selected by CARS was the best, and correlation coefficient of Calibration set (R_c) and root-mean-square error of Calibration set (RMSEC) of the model were 0.854 4 and 0.005 3, and correlation coefficient of prediction (R_p) and root-mean-square error of prediction set (RMSEP) of the model were 0.830 3 and 0.005 7, respectively, which indicated that CARS effectively reduced the dimension of the spectrum and simplified the data processing. The results showed that visible/near-infrared hyperspectral imaging technique combined with chemometrics methods and computer programming can effectively detect fructose content of Lingwu long jujube rapidly and non-destructively, providing a theoretical basis for the detection of internal quality of Lingwu long jujube.

Key words：Hyperspectral imaging technique; Fructose; Storage; High performance liquid chromatography; Partial least squares regression

Received: 2018-09-10 Accepted: 2019-01-22

ZTFLH:

TS255

Corresponding Authors: HE Jian-guo E-mail: hejg@nxu.edu.cn

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2019)10-3261-06 OR https://www.gpxygpfx.com/EN/Y2019/V39/I10/3261